Overview

Dataset statistics

Number of variables17
Number of observations9999
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory362.6 B

Variable types

Numeric12
Categorical5

Alerts

Date has a high cardinality: 305 distinct values High cardinality
AccountMng_Pages is highly correlated with AccountMng_DurationHigh correlation
AccountMng_Duration is highly correlated with AccountMng_PagesHigh correlation
FAQ_Pages is highly correlated with FAQ_DurationHigh correlation
FAQ_Duration is highly correlated with FAQ_PagesHigh correlation
Product_Pages is highly correlated with Product_Duration and 1 other fieldsHigh correlation
Product_Duration is highly correlated with Product_PagesHigh correlation
GoogleAnalytics_BounceRate is highly correlated with GoogleAnalytics_ExitRateHigh correlation
GoogleAnalytics_ExitRate is highly correlated with Product_Pages and 1 other fieldsHigh correlation
GoogleAnalytics_PageValue is highly correlated with BuyHigh correlation
Buy is highly correlated with GoogleAnalytics_PageValueHigh correlation
AccountMng_Pages is highly correlated with AccountMng_DurationHigh correlation
AccountMng_Duration is highly correlated with AccountMng_PagesHigh correlation
FAQ_Pages is highly correlated with FAQ_DurationHigh correlation
FAQ_Duration is highly correlated with FAQ_PagesHigh correlation
Product_Pages is highly correlated with Product_DurationHigh correlation
Product_Duration is highly correlated with Product_PagesHigh correlation
GoogleAnalytics_BounceRate is highly correlated with GoogleAnalytics_ExitRateHigh correlation
GoogleAnalytics_ExitRate is highly correlated with GoogleAnalytics_BounceRateHigh correlation
AccountMng_Pages is highly correlated with AccountMng_DurationHigh correlation
AccountMng_Duration is highly correlated with AccountMng_PagesHigh correlation
FAQ_Pages is highly correlated with FAQ_DurationHigh correlation
FAQ_Duration is highly correlated with FAQ_PagesHigh correlation
Product_Pages is highly correlated with Product_DurationHigh correlation
Product_Duration is highly correlated with Product_PagesHigh correlation
GoogleAnalytics_PageValue is highly correlated with BuyHigh correlation
Buy is highly correlated with GoogleAnalytics_PageValueHigh correlation
AccountMng_Pages is highly correlated with Product_PagesHigh correlation
AccountMng_Duration is highly correlated with FAQ_Duration and 1 other fieldsHigh correlation
FAQ_Pages is highly correlated with FAQ_Duration and 2 other fieldsHigh correlation
FAQ_Duration is highly correlated with AccountMng_Duration and 3 other fieldsHigh correlation
Product_Pages is highly correlated with AccountMng_Pages and 3 other fieldsHigh correlation
Product_Duration is highly correlated with AccountMng_Duration and 3 other fieldsHigh correlation
GoogleAnalytics_BounceRate is highly correlated with GoogleAnalytics_ExitRateHigh correlation
GoogleAnalytics_ExitRate is highly correlated with GoogleAnalytics_BounceRateHigh correlation
OS is highly correlated with Browser and 1 other fieldsHigh correlation
Browser is highly correlated with OS and 1 other fieldsHigh correlation
Type_of_Visitor is highly correlated with OS and 1 other fieldsHigh correlation
Access_ID has unique values Unique
AccountMng_Pages has 4672 (46.7%) zeros Zeros
AccountMng_Duration has 4785 (47.9%) zeros Zeros
FAQ_Pages has 7854 (78.5%) zeros Zeros
FAQ_Duration has 8030 (80.3%) zeros Zeros
Product_Duration has 628 (6.3%) zeros Zeros
GoogleAnalytics_BounceRate has 4471 (44.7%) zeros Zeros
GoogleAnalytics_PageValue has 7766 (77.7%) zeros Zeros

Reproduction

Analysis started2021-11-02 19:33:44.223722
Analysis finished2021-11-02 19:33:57.957910
Duration13.73 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Access_ID
Real number (ℝ≥0)

UNIQUE

Distinct9999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451972765.8
Minimum102863333
Maximum798444008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:58.016363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum102863333
5-th percentile136989382.8
Q1273194966
median453616869
Q3625975569.5
95-th percentile767250890.1
Maximum798444008
Range695580675
Interquartile range (IQR)352780603.5

Descriptive statistics

Standard deviation202498979.6
Coefficient of variation (CV)0.4480335874
Kurtosis-1.211334006
Mean451972765.8
Median Absolute Deviation (MAD)176832666
Skewness-0.003298733964
Sum4.519275685 × 1012
Variance4.100583676 × 1016
MonotonicityStrictly increasing
2021-11-02T19:33:58.098282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1028633331
 
< 0.1%
5705245791
 
< 0.1%
5702160201
 
< 0.1%
5702744321
 
< 0.1%
5703617971
 
< 0.1%
5704054871
 
< 0.1%
5704250831
 
< 0.1%
5704764951
 
< 0.1%
5705040511
 
< 0.1%
5706515281
 
< 0.1%
Other values (9989)9989
99.9%
ValueCountFrequency (%)
1028633331
< 0.1%
1031178141
< 0.1%
1032018911
< 0.1%
1032260871
< 0.1%
1032344451
< 0.1%
1032377671
< 0.1%
1032710401
< 0.1%
1032795851
< 0.1%
1033489921
< 0.1%
1033642591
< 0.1%
ValueCountFrequency (%)
7984440081
< 0.1%
7983712421
< 0.1%
7983398201
< 0.1%
7983141581
< 0.1%
7982795891
< 0.1%
7981645781
< 0.1%
7981364251
< 0.1%
7981045281
< 0.1%
7980132261
< 0.1%
7979558091
< 0.1%

Date
Categorical

HIGH CARDINALITY

Distinct305
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size641.7 KiB
25-May-20
 
120
5-May-20
 
111
7-May-20
 
110
14-May-20
 
110
8-May-20
 
109
Other values (300)
9439 

Length

Max length9
Median length9
Mean length8.706970697
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21-Mar-20
2nd row20-May-20
3rd row4-Dec-20
4th row22-Dec-20
5th row24-Nov-20

Common Values

ValueCountFrequency (%)
25-May-20120
 
1.2%
5-May-20111
 
1.1%
7-May-20110
 
1.1%
14-May-20110
 
1.1%
8-May-20109
 
1.1%
6-Nov-20107
 
1.1%
22-May-20106
 
1.1%
19-May-20106
 
1.1%
11-May-20106
 
1.1%
26-May-20105
 
1.1%
Other values (295)8909
89.1%

Length

2021-11-02T19:33:58.175848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25-may-20120
 
1.2%
5-may-20111
 
1.1%
7-may-20110
 
1.1%
14-may-20110
 
1.1%
8-may-20109
 
1.1%
6-nov-20107
 
1.1%
22-may-20106
 
1.1%
19-may-20106
 
1.1%
11-may-20106
 
1.1%
20-may-20105
 
1.1%
Other values (295)8909
89.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AccountMng_Pages
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.324232423
Minimum0
Maximum27
Zeros4672
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:58.238337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.34067596
Coefficient of variation (CV)1.437324394
Kurtosis4.850987396
Mean2.324232423
Median Absolute Deviation (MAD)1
Skewness1.97987619
Sum23240
Variance11.16011587
MonotonicityNot monotonic
2021-11-02T19:33:58.303924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
04672
46.7%
11103
 
11.0%
2900
 
9.0%
3735
 
7.4%
4628
 
6.3%
5458
 
4.6%
6357
 
3.6%
7277
 
2.8%
8230
 
2.3%
9180
 
1.8%
Other values (17)459
 
4.6%
ValueCountFrequency (%)
04672
46.7%
11103
 
11.0%
2900
 
9.0%
3735
 
7.4%
4628
 
6.3%
5458
 
4.6%
6357
 
3.6%
7277
 
2.8%
8230
 
2.3%
9180
 
1.8%
ValueCountFrequency (%)
271
 
< 0.1%
261
 
< 0.1%
244
 
< 0.1%
232
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
195
 
0.1%
1812
0.1%
1714
0.1%

AccountMng_Duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2838
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.20585434
Minimum0
Maximum3398.75
Zeros4785
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:58.374959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q392.20835
95-th percentile352.37003
Maximum3398.75
Range3398.75
Interquartile range (IQR)92.20835

Descriptive statistics

Standard deviation179.715545
Coefficient of variation (CV)2.213086069
Kurtosis49.5010676
Mean81.20585434
Median Absolute Deviation (MAD)7.5
Skewness5.605178491
Sum811977.3375
Variance32297.6771
MonotonicityNot monotonic
2021-11-02T19:33:58.453232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04785
47.9%
449
 
0.5%
1138
 
0.4%
737
 
0.4%
537
 
0.4%
634
 
0.3%
1431
 
0.3%
928
 
0.3%
1527
 
0.3%
1025
 
0.3%
Other values (2828)4908
49.1%
ValueCountFrequency (%)
04785
47.9%
1.33331
 
< 0.1%
213
 
0.1%
322
 
0.2%
3.52
 
< 0.1%
449
 
0.5%
4.51
 
< 0.1%
4.751
 
< 0.1%
537
 
0.4%
5.06671
 
< 0.1%
ValueCountFrequency (%)
3398.751
< 0.1%
2657.31811
< 0.1%
2629.2541
< 0.1%
2407.42381
< 0.1%
2137.11271
< 0.1%
2086.751
< 0.1%
2047.23481
< 0.1%
1951.27911
< 0.1%
19461
< 0.1%
19221
< 0.1%

FAQ_Pages
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5080508051
Minimum0
Maximum24
Zeros7854
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:58.523115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.279389526
Coefficient of variation (CV)2.51823147
Kurtosis28.77693983
Mean0.5080508051
Median Absolute Deviation (MAD)0
Skewness4.107353861
Sum5080
Variance1.636837559
MonotonicityNot monotonic
2021-11-02T19:33:58.582733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
07854
78.5%
1844
 
8.4%
2592
 
5.9%
3313
 
3.1%
4180
 
1.8%
584
 
0.8%
667
 
0.7%
730
 
0.3%
913
 
0.1%
88
 
0.1%
Other values (7)14
 
0.1%
ValueCountFrequency (%)
07854
78.5%
1844
 
8.4%
2592
 
5.9%
3313
 
3.1%
4180
 
1.8%
584
 
0.8%
667
 
0.7%
730
 
0.3%
88
 
0.1%
913
 
0.1%
ValueCountFrequency (%)
241
 
< 0.1%
161
 
< 0.1%
142
 
< 0.1%
131
 
< 0.1%
124
 
< 0.1%
111
 
< 0.1%
104
 
< 0.1%
913
0.1%
88
 
0.1%
730
0.3%

FAQ_Duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1089
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.55910121
Minimum0
Maximum2549.375
Zeros8030
Zeros (%)80.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:58.649522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile200.1
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation139.796989
Coefficient of variation (CV)4.045156966
Kurtosis78.14365909
Mean34.55910121
Median Absolute Deviation (MAD)0
Skewness7.627079075
Sum345556.453
Variance19543.19814
MonotonicityNot monotonic
2021-11-02T19:33:58.728040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08030
80.3%
925
 
0.3%
1021
 
0.2%
1220
 
0.2%
620
 
0.2%
720
 
0.2%
1120
 
0.2%
1319
 
0.2%
1618
 
0.2%
818
 
0.2%
Other values (1079)1788
 
17.9%
ValueCountFrequency (%)
08030
80.3%
13
 
< 0.1%
1.51
 
< 0.1%
28
 
0.1%
2.51
 
< 0.1%
314
 
0.1%
415
 
0.2%
512
 
0.1%
5.52
 
< 0.1%
620
 
0.2%
ValueCountFrequency (%)
2549.3751
< 0.1%
2256.91671
< 0.1%
2195.31
< 0.1%
2166.51
< 0.1%
2050.43331
< 0.1%
1949.16671
< 0.1%
1830.51
< 0.1%
1779.16671
< 0.1%
17781
< 0.1%
1767.66671
< 0.1%

Product_Pages
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct291
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.68586859
Minimum0
Maximum705
Zeros33
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:58.804690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.55027695
Coefficient of variation (CV)1.405998287
Kurtosis33.12990553
Mean31.68586859
Median Absolute Deviation (MAD)13
Skewness4.448047888
Sum316827
Variance1984.727177
MonotonicityNot monotonic
2021-11-02T19:33:58.945767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1522
 
5.2%
2373
 
3.7%
3367
 
3.7%
4336
 
3.4%
6319
 
3.2%
7315
 
3.2%
5310
 
3.1%
8293
 
2.9%
10269
 
2.7%
12255
 
2.6%
Other values (281)6640
66.4%
ValueCountFrequency (%)
033
 
0.3%
1522
5.2%
2373
3.7%
3367
3.7%
4336
3.4%
5310
3.1%
6319
3.2%
7315
3.2%
8293
2.9%
9247
2.5%
ValueCountFrequency (%)
7051
< 0.1%
6861
< 0.1%
5841
< 0.1%
5181
< 0.1%
5171
< 0.1%
5011
< 0.1%
4861
< 0.1%
4701
< 0.1%
4491
< 0.1%
4401
< 0.1%

Product_Duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7884
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1199.76943
Minimum0
Maximum63973.5222
Zeros628
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:59.024846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1183.5625
median599
Q31470.2708
95-th percentile4284.7764
Maximum63973.5222
Range63973.5222
Interquartile range (IQR)1286.7083

Descriptive statistics

Standard deviation1958.276304
Coefficient of variation (CV)1.632210536
Kurtosis151.7253116
Mean1199.76943
Median Absolute Deviation (MAD)501.5
Skewness7.810046833
Sum11996494.53
Variance3834846.083
MonotonicityNot monotonic
2021-11-02T19:33:59.105691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0628
 
6.3%
1719
 
0.2%
815
 
0.2%
1115
 
0.2%
1514
 
0.1%
1913
 
0.1%
1213
 
0.1%
2212
 
0.1%
5912
 
0.1%
512
 
0.1%
Other values (7874)9246
92.5%
ValueCountFrequency (%)
0628
6.3%
0.51
 
< 0.1%
11
 
< 0.1%
2.33331
 
< 0.1%
2.66671
 
< 0.1%
33
 
< 0.1%
45
 
0.1%
512
 
0.1%
5.33331
 
< 0.1%
64
 
< 0.1%
ValueCountFrequency (%)
63973.52221
< 0.1%
43171.23341
< 0.1%
29970.4661
< 0.1%
27009.85941
< 0.1%
24844.15621
< 0.1%
23888.811
< 0.1%
23342.08211
< 0.1%
23050.10411
< 0.1%
21857.04651
< 0.1%
21672.24431
< 0.1%

GoogleAnalytics_BounceRate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct516
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02230545055
Minimum0
Maximum0.2
Zeros4471
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:59.190296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0032
Q30.0168
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.0168

Descriptive statistics

Standard deviation0.04877597427
Coefficient of variation (CV)2.186728942
Kurtosis7.648446986
Mean0.02230545055
Median Absolute Deviation (MAD)0.0032
Skewness2.940060119
Sum223.0322
Variance0.002379095666
MonotonicityNot monotonic
2021-11-02T19:33:59.272463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04471
44.7%
0.2581
 
5.8%
0.0667113
 
1.1%
0.028695
 
1.0%
0.0587
 
0.9%
0.033382
 
0.8%
0.0481
 
0.8%
0.02580
 
0.8%
0.180
 
0.8%
0.012579
 
0.8%
Other values (506)4250
42.5%
ValueCountFrequency (%)
04471
44.7%
0.00017
 
0.1%
0.00028
 
0.1%
0.000310
 
0.1%
0.00048
 
0.1%
0.00056
 
0.1%
0.000611
 
0.1%
0.000710
 
0.1%
0.000815
 
0.2%
0.000912
 
0.1%
ValueCountFrequency (%)
0.2581
5.8%
0.18331
 
< 0.1%
0.184
 
< 0.1%
0.1751
 
< 0.1%
0.16673
 
< 0.1%
0.16431
 
< 0.1%
0.163
 
< 0.1%
0.15563
 
< 0.1%
0.1511
 
0.1%
0.14671
 
< 0.1%

GoogleAnalytics_ExitRate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct857
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04318146815
Minimum0
Maximum0.2
Zeros61
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:59.357485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0048
Q10.0143
median0.0251
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.0357

Descriptive statistics

Standard deviation0.04884527615
Coefficient of variation (CV)1.131162933
Kurtosis3.985723873
Mean0.04318146815
Median Absolute Deviation (MAD)0.0142
Skewness2.146976695
Sum431.7715
Variance0.002385861002
MonotonicityNot monotonic
2021-11-02T19:33:59.438852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2591
 
5.9%
0.1274
 
2.7%
0.05274
 
2.7%
0.0333230
 
2.3%
0.0667222
 
2.2%
0.025179
 
1.8%
0.04177
 
1.8%
0.0167156
 
1.6%
0.02136
 
1.4%
0.0222135
 
1.4%
Other values (847)7625
76.3%
ValueCountFrequency (%)
061
0.6%
0.00034
 
< 0.1%
0.00041
 
< 0.1%
0.00054
 
< 0.1%
0.00062
 
< 0.1%
0.00074
 
< 0.1%
0.00083
 
< 0.1%
0.00091
 
< 0.1%
0.0015
 
0.1%
0.00114
 
< 0.1%
ValueCountFrequency (%)
0.2591
5.9%
0.18892
 
< 0.1%
0.18673
 
< 0.1%
0.18331
 
< 0.1%
0.18181
 
< 0.1%
0.182
 
< 0.1%
0.17784
 
< 0.1%
0.1754
 
< 0.1%
0.17381
 
< 0.1%
0.17331
 
< 0.1%

GoogleAnalytics_PageValue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2203
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.963120292
Minimum0
Maximum361.7637
Zeros7766
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:59.523449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.61663
Maximum361.7637
Range361.7637
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.75362571
Coefficient of variation (CV)3.144935
Kurtosis67.18704665
Mean5.963120292
Median Absolute Deviation (MAD)0
Skewness6.430619989
Sum59625.2398
Variance351.6984774
MonotonicityNot monotonic
2021-11-02T19:33:59.603379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07766
77.7%
53.9885
 
0.1%
42.29313
 
< 0.1%
15.39562
 
< 0.1%
54.982
 
< 0.1%
4.51112
 
< 0.1%
44.89352
 
< 0.1%
5.69862
 
< 0.1%
9.64112
 
< 0.1%
26.54552
 
< 0.1%
Other values (2193)2211
 
22.1%
ValueCountFrequency (%)
07766
77.7%
0.0381
 
< 0.1%
0.0671
 
< 0.1%
0.09351
 
< 0.1%
0.12071
 
< 0.1%
0.12971
 
< 0.1%
0.13181
 
< 0.1%
0.13921
 
< 0.1%
0.15071
 
< 0.1%
0.15221
 
< 0.1%
ValueCountFrequency (%)
361.76371
< 0.1%
360.95341
< 0.1%
287.95381
< 0.1%
270.78471
< 0.1%
261.49131
< 0.1%
254.60721
< 0.1%
246.75861
< 0.1%
239.981
< 0.1%
226.67771
< 0.1%
218.39521
< 0.1%

OS
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size621.5 KiB
Windows
5361 
Android
2104 
MacOSX
2065 
iOS
 
378
Ubuntu
 
68
Other values (3)
 
23

Length

Max length9
Median length7
Mean length6.637163716
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMacOSX
2nd rowWindows
3rd rowWindows
4th rowWindows
5th rowWindows

Common Values

ValueCountFrequency (%)
Windows5361
53.6%
Android2104
 
21.0%
MacOSX2065
 
20.7%
iOS378
 
3.8%
Ubuntu68
 
0.7%
Chrome OS15
 
0.2%
Other5
 
0.1%
Fedora3
 
< 0.1%

Length

2021-11-02T19:33:59.682578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-02T19:33:59.730650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
windows5361
53.5%
android2104
 
21.0%
macosx2065
 
20.6%
ios378
 
3.8%
ubuntu68
 
0.7%
chrome15
 
0.1%
os15
 
0.1%
other5
 
< 0.1%
fedora3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Browser
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.352535254
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:33:59.787360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.715698122
Coefficient of variation (CV)0.7292975184
Kurtosis13.01326822
Mean2.352535254
Median Absolute Deviation (MAD)0
Skewness3.276346893
Sum23523
Variance2.943620047
MonotonicityNot monotonic
2021-11-02T19:33:59.846087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
26484
64.8%
11990
 
19.9%
4597
 
6.0%
5362
 
3.6%
6138
 
1.4%
10130
 
1.3%
8113
 
1.1%
381
 
0.8%
1352
 
0.5%
741
 
0.4%
Other values (2)11
 
0.1%
ValueCountFrequency (%)
11990
 
19.9%
26484
64.8%
381
 
0.8%
4597
 
6.0%
5362
 
3.6%
6138
 
1.4%
741
 
0.4%
8113
 
1.1%
10130
 
1.3%
113
 
< 0.1%
ValueCountFrequency (%)
1352
 
0.5%
128
 
0.1%
113
 
< 0.1%
10130
 
1.3%
8113
 
1.1%
741
 
0.4%
6138
 
1.4%
5362
3.6%
4597
6.0%
381
 
0.8%

Country
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size624.7 KiB
Portugal
3870 
Spain
1945 
Brazil
947 
France
923 
Other
659 
Other values (4)
1655 

Length

Max length14
Median length6
Mean length6.96209621
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPortugal
2nd rowFrance
3rd rowItaly
4th rowUnited Kingdom
5th rowSpain

Common Values

ValueCountFrequency (%)
Portugal3870
38.7%
Spain1945
19.5%
Brazil947
 
9.5%
France923
 
9.2%
Other659
 
6.6%
Italy613
 
6.1%
United Kingdom429
 
4.3%
Germany350
 
3.5%
Switzerland263
 
2.6%

Length

2021-11-02T19:33:59.907055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-02T19:33:59.951038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
portugal3870
37.1%
spain1945
18.7%
brazil947
 
9.1%
france923
 
8.9%
other659
 
6.3%
italy613
 
5.9%
united429
 
4.1%
kingdom429
 
4.1%
germany350
 
3.4%
switzerland263
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Type_of_Traffic
Real number (ℝ≥0)

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.95749575
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-11-02T19:34:00.012021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.67515917
Coefficient of variation (CV)0.9286577682
Kurtosis1.410794103
Mean3.95749575
Median Absolute Deviation (MAD)1
Skewness1.612960454
Sum39571
Variance13.50679493
MonotonicityNot monotonic
2021-11-02T19:34:00.137760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
23150
31.5%
12008
20.1%
31676
16.8%
4870
 
8.7%
13595
 
6.0%
10355
 
3.6%
6354
 
3.5%
8284
 
2.8%
5210
 
2.1%
11205
 
2.1%
Other values (5)292
 
2.9%
ValueCountFrequency (%)
12008
20.1%
23150
31.5%
31676
16.8%
4870
 
8.7%
5210
 
2.1%
6354
 
3.5%
731
 
0.3%
8284
 
2.8%
934
 
0.3%
10355
 
3.6%
ValueCountFrequency (%)
15156
 
1.6%
1427
 
0.3%
13595
6.0%
1244
 
0.4%
11205
 
2.1%
10355
3.6%
934
 
0.3%
8284
2.8%
731
 
0.3%
6354
3.5%

Type_of_Visitor
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size637.3 KiB
Returner
8534 
New_Access
1391 
Other
 
74

Length

Max length10
Median length8
Mean length8.256025603
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturner
2nd rowReturner
3rd rowReturner
4th rowReturner
5th rowReturner

Common Values

ValueCountFrequency (%)
Returner8534
85.3%
New_Access1391
 
13.9%
Other74
 
0.7%

Length

2021-11-02T19:34:00.205026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-02T19:34:00.247213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
returner8534
85.3%
new_access1391
 
13.9%
other74
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Buy
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
8447 
1
1552 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
08447
84.5%
11552
 
15.5%

Length

2021-11-02T19:34:00.291968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-02T19:34:00.329855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08447
84.5%
11552
 
15.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-02T19:33:56.629571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.167481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.159091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.032471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.998023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.856561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.818161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.793971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.757091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.796002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.767883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.765942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.706129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.258700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.235008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.108919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.071175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.930734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.894519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.941631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.836328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.878519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.846051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.836703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.777071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.329990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.304857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.183849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.139808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.070547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.968705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.014539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.910359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.955307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.919987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.903912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.852935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.405850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.377803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.326736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.213962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.146899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.046978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.089365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.990643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.037538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.997974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.975379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.924495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.551113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.446888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.397889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.282668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.217707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.122750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.161507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.066428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.114616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.072501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.042233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.995561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.624486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.515975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.468545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.349660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.287001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.200809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.232495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.142049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.193063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.147888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.108716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:57.072638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.699049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.592174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.544770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.421346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.362178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.284292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.307738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.225032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.276510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.227308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.182934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:57.147662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.773605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.664572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.618445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.491470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.437778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.364117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.379633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.305590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.356203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.302676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.253443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:57.230139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.858385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.743544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.698974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.568826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.518481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.467825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.458985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.395747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.442969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.383951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.331640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:57.310280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:46.938075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.820875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.778151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.647697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.597625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.558952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.538355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.481179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.527694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.465029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.411261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:57.456752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.013161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.894088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.854200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.720371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.673737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.639522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.614328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.565240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.610385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.615308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.487564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:57.524371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.085410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:47.962090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:48.924316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:49.787114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:50.743627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:51.713068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:52.684233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:53.644288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:54.687542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:55.690282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-02T19:33:56.557265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-02T19:34:00.377692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-02T19:34:00.519776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-02T19:34:00.658580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-02T19:34:00.784871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-02T19:34:00.874556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-02T19:33:57.668473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-02T19:33:57.857970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Access_IDDateAccountMng_PagesAccountMng_DurationFAQ_PagesFAQ_DurationProduct_PagesProduct_DurationGoogleAnalytics_BounceRateGoogleAnalytics_ExitRateGoogleAnalytics_PageValueOSBrowserCountryType_of_TrafficType_of_VisitorBuy
010286333321-Mar-2000.000000.0364.00000.00000.06670.0000MacOSX2Portugal1Returner0
110311781420-May-2000.000000.023684.50000.02170.04490.0000Windows2France6Returner0
21032018914-Dec-2000.000000.0895.00000.02500.05830.0000Windows4Italy1Returner0
310322608722-Dec-2000.000000.09608.75000.00000.025042.4225Windows2United Kingdom2Returner1
410323444524-Nov-2000.00002386.0361609.93970.00000.009312.5033Windows2Spain3Returner1
51032377674-Mar-20313.000000.028324.76920.00000.00170.0000Windows2Portugal6Returner0
61032710403-Nov-2015270.01673122.0662780.60000.00000.01650.0000Windows2Spain10Returner0
710327958519-Mar-2000.000000.012249.50000.00560.02500.0000Windows7Italy1Returner0
810334899226-Mar-2000.000000.0330.00000.00000.06670.0000Windows2Portugal1Returner0
910336425913-Mar-2000.000000.017469.41670.00000.01180.0000Android1France2Returner0

Last rows

Access_IDDateAccountMng_PagesAccountMng_DurationFAQ_PagesFAQ_DurationProduct_PagesProduct_DurationGoogleAnalytics_BounceRateGoogleAnalytics_ExitRateGoogleAnalytics_PageValueOSBrowserCountryType_of_TrafficType_of_VisitorBuy
998979795580922-Nov-20791.250010.0952900.79170.00400.02334.4857Windows2Portugal2Returner1
999079801322618-May-20857.833313.01158388.31610.01250.02950.0000MacOSX2France6Returner0
99917981045284-Dec-206461.000000.04111.00000.00000.01110.0000MacOSX3Spain8New_Access0
999279813642518-May-2000.000000.020289.75000.00000.02500.0000Android1Brazil3Returner0
99937981645788-May-2000.000000.016462.73330.01250.04690.0000Android1Portugal3Returner0
99947982795898-May-203159.0000255.5231100.12500.00000.01110.0000MacOSX2Brazil14Returner0
999579831415817-Jul-206175.100000.0327.60000.00000.01110.0000Windows10Spain5New_Access0
999679833982023-Mar-2000.000000.027644.00000.00770.05190.0000MacOSX2France3Returner0
999779837124216-May-2000.000000.053715.50000.02260.03630.0000Windows2Italy3Returner0
999879844400820-Nov-2000.000000.0231919.55000.00870.03194.2803Windows2Brazil1Returner0